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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: prosody_gtsc_phi-3-mini |
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results: |
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- task: |
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type: dialogue act classification |
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dataset: |
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name: asapp/slue-phase-2 |
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type: hvb |
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metrics: |
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- name: F1 macro E2E |
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type: F1 macro |
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value: 67.75 |
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- name: F1 macro GT |
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type: F1 macro |
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value: 72.74 |
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datasets: |
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- asapp/slue-phase-2 |
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language: |
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- en |
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metrics: |
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- f1-macro |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# prosody_gtsc_phi-3-mini |
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Ground truth text with prosody encoding residual cross attention multi-label DAC |
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## Model description |
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Prosody encoder: 2 layer transformer encoder with initial dense projection |
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Backbone: [Phi 3 mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) |
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Pooling: Self attention |
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Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween |
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## Training and evaluation data |
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Trained on ground truth. |
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Evaluated on ground truth (GT) and normalized [Whisper small](https://huggingface.co/openai/whisper-small) transcripts (E2E). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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